Benchmarking 2D Multi-Object Detection and Tracking Algorithms in Autonomous Vehicle Driving Scenarios.

Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and other vehicles on the road. The experimental analyses conducted so far have not thoroughly evaluated the effectiveness of these methods in road driving scenarios. To this aim, we propose in this paper a benchmark of modern multi-object detection and tracking methods applied to image sequences acquired by a camera installed on board the vehicle, namely, on the videos available in the BDD100K dataset. The proposed experimental framework allows to evaluate 22 different combinations of multi-object detection and tracking methods using metrics that highlight the positive contribution and limitations of each module of the considered algorithms. The analysis of the experimental results points out that the best method currently available is the combination of ConvNext and QDTrack, but also that the multi-object tracking methods applied on road images must be substantially improved. Thanks to our analysis, we conclude that the evaluation metrics should be extended by considering specific aspects of the autonomous driving scenarios, such as multi-class problem formulation and distance from the targets, and that the effectiveness of the methods must be evaluated by simulating the impact of the errors on driving safety.

SILA: a system for scientific image analysis.

A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset.

A New Unsupervised Approach for Segmenting and Counting Cells in High-Throughput Microscopy Image Sets.

New technological advances in automated microscopy have given rise to large volumes of data, which have made human-based analysis infeasible, heightening the need for automatic systems for high-throughput microscopy applications. In particular, in the field of fluorescence microscopy, automatic tools for image analysis are making an essential contribution in order to increase the statistical power of the cell analysis process. The development of these automatic systems is a difficult task due to both the diversification of the staining patterns and the local variability of the images. In this paper, we present an unsupervised approach for automatic cell segmentation and counting, namely CSC, in high-throughput microscopy images. The segmentation is performed by dividing the whole image into square patches that undergo a gray level clustering followed by an adaptive thresholding. Subsequently, the cell labeling is obtained by detecting the centers of the cells, using both distance transform and curvature analysis, and by applying a region growing process. The advantages of CSC are manifold. The foreground detection process works on gray levels rather than on individual pixels, so it proves to be very efficient. Moreover, the combination of distance transform and curvature analysis makes the counting process very robust to clustered cells. A further strength of the CSC method is the limited number of parameters that must be tuned. Indeed, two different versions of the method have been considered, CSC-7 and CSC-3, depending on the number of parameters to be tuned. The CSC method has been tested on several publicly available image datasets of real and synthetic images. Results in terms of standard metrics and spatially aware measures show that CSC outperforms the current state-of-the-art techniques.

Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status.

BACKGROUND AND AIM: Lung ultrasound has been used to describe common respiratory diseases both by visual and computer-assisted gray scale analysis. In the present paper, we compare both methods in assessing neonatal respiratory status keeping two oxygenation indexes as standards. PATIENTS AND METHODS: Neonates admitted to the NICU for respiratory distress were enrolled. Two neonatologists not attending the patients performed a lung scan, built a single frame database and rated the images with a standardized score. The same dataset was processed using the gray scale analysis implemented with textural features and machine learning analysis. Both the oxygenation ratio (PaO2/FiO2) and the alveolar arterial oxygen gradient (A-a) were kept as reference standards. RESULTS: Seventy-five neonates with different respiratory status were enrolled in the study and a dataset of 600 ultrasound frames was built. Visual assessment of respiratory status correlated significantly with PaO2/FiO2 (r = -0.55; p<0.0001) and the A-a (r = 0.59; p<0.0001) with a strong interobserver agreement (K = 0.91). A significant correlation was also found between both oxygenation indexes and the gray scale analysis of lung ultrasound scans using regions of interest corresponding to 50K (r = -0.42; p<0.002 for PaO2/FiO2; r = 0.46 p<0.001 for A-a) and 100K (r = -0.35 p<0.01 for PaO2/FiO2; r = 0.58 p<0.0001 for A-a) pixels regions of interest. CONCLUSIONS: A semi quantitative estimate of the degree of neonatal respiratory distress was demonstrated both by a validated scoring system and by computer assisted analysis of the ultrasound scan. This data may help to implement point of care ultrasound diagnostics in the NICU.

Automatic segmentation of pigment deposits in retinal fundus images of Retinitis Pigmentosa.

Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images. Fundus image analysis is a suitable tool in daily practice for an early detection of retinal diseases and the monitoring of their progression. Moreover, the fundus camera represents a low-cost and easy-access diagnostic system, which can be employed in resource-limited regions and countries. The fundus images of a patient suffering from retinitis pigmentosa are characterized by an attenuation of the vessels, a waxy disc pallor and the presence of pigment deposits. Considering that several methods have been proposed for the analysis of retinal vessels and the optic disk, this work focuses on the automatic segmentation of the pigment deposits in the fundus images. The image distortions are attenuated by applying a local pre-processing. Next, a watershed transformation is carried out to produce homogeneous regions. Working on regions rather than on pixels makes the method very robust to the high variability of pigment deposits in terms of color and shape, so allowing the detection even of small pigment deposits. The regions undergo a feature extraction procedure, so that a region classification process is performed by means of an outlier detection analysis and a rule set. The experiments have been performed on a dataset of images of patients suffering from retinitis pigmentosa. Although the images present a high variability in terms of color and illumination, the method provides a good performance in terms of sensitivity, specificity, accuracy and the F-measure, whose values are 74.43, 98.44, 97.90, 59.04, respectively.